Types of research methods
• Experiments
• Interviews
• Questionnaires
• Obser vation
• Correlation
• Twin studies
• Case studies
• Meta analysis
• Content analysis
,Analysis and interpretation of correlations
• Experiment - includes and IV and DV and includes
the manipulation of the IV to measure the DV
• Correlation - includes t wo DVs - in a correlation
there is no manipulation
Analysis and interpretation of correlation
Correlation and correlation coefficient
• correlation refers to a mathematical technique
which measures the relationship/ association
bet ween t wo continuous variables (properly
called co-variables).
• Such relationships are plotted on a scattergram
where each axis represents one of the variable
investigated
Working out what a coefficient means
• A value of +1 represents a perfect positive
correlation and a value of -1, a perfect negative
correlation.
• The close the coefficient is to +1 or -1, the stronger
the relationship bet ween the co-variables is
• The closed to zero, the weaker the relationship is
+.50 is as strong relationship as -.50, the sign just
indicates the direction
• However, it should be noted that coefficient that
appear to indicate weak correlation can still be
statistically significant - it depends on the size of
the data set
, Evaluation of types of correlation
Strengths
• The data may be easily available for research to quickly analyse
• This is a strength as it enables the research to access large amounts of data that would other wise be
impossible to gather if they tried to amass this from scratch
• Large amounts of quantitative data mean that the research is high reliability
• Correlation allow researchers to make predictions as to the relationship bet ween co-variables e.g.
knowing there is a relationship bet ween school absence and GCSE results could be used to identify
students at risk and to implement interventions to help them achieve their potential
Limitations
• Extraneous factors connected to one or both co-variables
may affect the result and lead to invalidconclusions being
made e.g. number of days absence from school may be due to
illness rather than to choice
• A low GCSE score may be due to a high turnover of teachers in
one school rather than to student absence
• Correlations work well for linear relationships e.g. height and
shoe size
• They are less successful when dealing with non-linear
relationships e.g. number of hours worked and level of
happiness
• This limits the type of data that can be analysed and
conclusion drawn
• Experiments
• Interviews
• Questionnaires
• Obser vation
• Correlation
• Twin studies
• Case studies
• Meta analysis
• Content analysis
,Analysis and interpretation of correlations
• Experiment - includes and IV and DV and includes
the manipulation of the IV to measure the DV
• Correlation - includes t wo DVs - in a correlation
there is no manipulation
Analysis and interpretation of correlation
Correlation and correlation coefficient
• correlation refers to a mathematical technique
which measures the relationship/ association
bet ween t wo continuous variables (properly
called co-variables).
• Such relationships are plotted on a scattergram
where each axis represents one of the variable
investigated
Working out what a coefficient means
• A value of +1 represents a perfect positive
correlation and a value of -1, a perfect negative
correlation.
• The close the coefficient is to +1 or -1, the stronger
the relationship bet ween the co-variables is
• The closed to zero, the weaker the relationship is
+.50 is as strong relationship as -.50, the sign just
indicates the direction
• However, it should be noted that coefficient that
appear to indicate weak correlation can still be
statistically significant - it depends on the size of
the data set
, Evaluation of types of correlation
Strengths
• The data may be easily available for research to quickly analyse
• This is a strength as it enables the research to access large amounts of data that would other wise be
impossible to gather if they tried to amass this from scratch
• Large amounts of quantitative data mean that the research is high reliability
• Correlation allow researchers to make predictions as to the relationship bet ween co-variables e.g.
knowing there is a relationship bet ween school absence and GCSE results could be used to identify
students at risk and to implement interventions to help them achieve their potential
Limitations
• Extraneous factors connected to one or both co-variables
may affect the result and lead to invalidconclusions being
made e.g. number of days absence from school may be due to
illness rather than to choice
• A low GCSE score may be due to a high turnover of teachers in
one school rather than to student absence
• Correlations work well for linear relationships e.g. height and
shoe size
• They are less successful when dealing with non-linear
relationships e.g. number of hours worked and level of
happiness
• This limits the type of data that can be analysed and
conclusion drawn
